BICO: BIRCH Meets Coresets for k-Means Clustering

DOI10.1007/978-3-642-40450-4_41zbMath1395.68360OpenAlexW2220402431MaRDI QIDQ2849339

Hendrik Fichtenberger, Christian Sohler, Melanie Schmidt, Chris Schwiegelshohn, Marc Gillé

Publication date: 17 September 2013

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-40450-4_41

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Data structures (68P05) Algorithms in computer science (68W99)

Related Items (5)

Turning Grain Maps into Diagrams ⋮ Accurate Low-Space Approximation of Metric k-Median for Insertion-Only Streams ⋮ Metric \(k\)-median clustering in insertion-only streams ⋮ Turning Big Data Into Tiny Data: Constant-Size Coresets for $k$-Means, PCA, and Projective Clustering ⋮ Statistical hierarchical clustering algorithm for outlier detection in evolving data streams

Uses Software

• UCI-ml
• k-means++
• StreamKM++
• ESMERALDA

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